Overview

Dataset statistics

Number of variables13
Number of observations5367
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)0.1%
Total size in memory545.2 KiB
Average record size in memory104.0 B

Variable types

Categorical2
Numeric11

Alerts

Dataset has 6 (0.1%) duplicate rowsDuplicates
forumng is highly overall correlated with homepage and 2 other fieldsHigh correlation
homepage is highly overall correlated with forumng and 4 other fieldsHigh correlation
oucollaborate is highly overall correlated with oucontent and 1 other fieldsHigh correlation
oucontent is highly overall correlated with oucollaborate and 1 other fieldsHigh correlation
questionnaire is highly overall correlated with oucollaborate and 1 other fieldsHigh correlation
quiz is highly overall correlated with homepage and 2 other fieldsHigh correlation
resource is highly overall correlated with homepage and 2 other fieldsHigh correlation
subpage is highly overall correlated with forumng and 4 other fieldsHigh correlation
url is highly overall correlated with forumng and 2 other fieldsHigh correlation
sharedsubpage is highly imbalanced (92.8%)Imbalance
resource is highly skewed (γ1 = 21.61052983)Skewed
forumng has 475 (8.9%) zerosZeros
glossary has 3336 (62.2%) zerosZeros
oucollaborate has 3754 (69.9%) zerosZeros
oucontent has 836 (15.6%) zerosZeros
ouelluminate has 4962 (92.5%) zerosZeros
questionnaire has 4660 (86.8%) zerosZeros
quiz has 534 (9.9%) zerosZeros
resource has 171 (3.2%) zerosZeros
subpage has 101 (1.9%) zerosZeros
url has 626 (11.7%) zerosZeros

Reproduction

Analysis started2023-04-04 21:07:10.981212
Analysis finished2023-04-04 21:07:30.423659
Duration19.44 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

final_result
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.1 KiB
Pass
3752 
Fail
1615 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21468
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFail
2nd rowPass
3rd rowFail
4th rowPass
5th rowPass

Common Values

ValueCountFrequency (%)
Pass 3752
69.9%
Fail 1615
30.1%

Length

2023-04-04T17:07:30.511781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-04T17:07:30.657378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
pass 3752
69.9%
fail 1615
30.1%

Most occurring characters

ValueCountFrequency (%)
s 7504
35.0%
a 5367
25.0%
P 3752
17.5%
F 1615
 
7.5%
i 1615
 
7.5%
l 1615
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16101
75.0%
Uppercase Letter 5367
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 7504
46.6%
a 5367
33.3%
i 1615
 
10.0%
l 1615
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
P 3752
69.9%
F 1615
30.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 21468
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 7504
35.0%
a 5367
25.0%
P 3752
17.5%
F 1615
 
7.5%
i 1615
 
7.5%
l 1615
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 7504
35.0%
a 5367
25.0%
P 3752
17.5%
F 1615
 
7.5%
i 1615
 
7.5%
l 1615
 
7.5%

forumng
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1195
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean371.59735
Minimum0
Maximum13154
Zeros475
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:30.846503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median110
Q3341
95-th percentile1508.4
Maximum13154
Range13154
Interquartile range (IQR)319

Descriptive statistics

Standard deviation884.07207
Coefficient of variation (CV)2.3791129
Kurtosis54.402499
Mean371.59735
Median Absolute Deviation (MAD)105
Skewness6.3568522
Sum1994363
Variance781583.43
MonotonicityNot monotonic
2023-04-04T17:07:31.032050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
 
8.9%
1 86
 
1.6%
2 65
 
1.2%
6 58
 
1.1%
3 55
 
1.0%
9 52
 
1.0%
7 51
 
1.0%
4 50
 
0.9%
8 48
 
0.9%
5 41
 
0.8%
Other values (1185) 4386
81.7%
ValueCountFrequency (%)
0 475
8.9%
1 86
 
1.6%
2 65
 
1.2%
3 55
 
1.0%
4 50
 
0.9%
5 41
 
0.8%
6 58
 
1.1%
7 51
 
1.0%
8 48
 
0.9%
9 52
 
1.0%
ValueCountFrequency (%)
13154 1
< 0.1%
11465 1
< 0.1%
11344 1
< 0.1%
10555 1
< 0.1%
10483 1
< 0.1%
9919 1
< 0.1%
9666 1
< 0.1%
9560 1
< 0.1%
9552 1
< 0.1%
9279 1
< 0.1%

glossary
Real number (ℝ)

Distinct45
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7121297
Minimum0
Maximum134
Zeros3336
Zeros (%)62.2%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:31.181504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8
Maximum134
Range134
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.9684476
Coefficient of variation (CV)2.9019108
Kurtosis205.55198
Mean1.7121297
Median Absolute Deviation (MAD)0
Skewness10.689986
Sum9189
Variance24.685472
MonotonicityNot monotonic
2023-04-04T17:07:31.328897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 3336
62.2%
1 670
 
12.5%
2 405
 
7.5%
3 215
 
4.0%
4 163
 
3.0%
5 126
 
2.3%
6 83
 
1.5%
7 65
 
1.2%
8 52
 
1.0%
9 36
 
0.7%
Other values (35) 216
 
4.0%
ValueCountFrequency (%)
0 3336
62.2%
1 670
 
12.5%
2 405
 
7.5%
3 215
 
4.0%
4 163
 
3.0%
5 126
 
2.3%
6 83
 
1.5%
7 65
 
1.2%
8 52
 
1.0%
9 36
 
0.7%
ValueCountFrequency (%)
134 1
 
< 0.1%
124 1
 
< 0.1%
88 1
 
< 0.1%
84 1
 
< 0.1%
51 1
 
< 0.1%
46 2
< 0.1%
39 3
0.1%
37 2
< 0.1%
36 2
< 0.1%
35 2
< 0.1%

homepage
Real number (ℝ)

Distinct771
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.64822
Minimum0
Maximum4120
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:31.494761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q160
median137
Q3248
95-th percentile584
Maximum4120
Range4120
Interquartile range (IQR)188

Descriptive statistics

Standard deviation251.35238
Coefficient of variation (CV)1.2589763
Kurtosis46.934453
Mean199.64822
Median Absolute Deviation (MAD)86
Skewness5.1252111
Sum1071512
Variance63178.021
MonotonicityNot monotonic
2023-04-04T17:07:31.669528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 33
 
0.6%
6 32
 
0.6%
29 32
 
0.6%
18 30
 
0.6%
123 30
 
0.6%
47 29
 
0.5%
2 29
 
0.5%
3 29
 
0.5%
1 28
 
0.5%
46 28
 
0.5%
Other values (761) 5067
94.4%
ValueCountFrequency (%)
0 7
 
0.1%
1 28
0.5%
2 29
0.5%
3 29
0.5%
4 25
0.5%
5 24
0.4%
6 32
0.6%
7 16
0.3%
8 22
0.4%
9 23
0.4%
ValueCountFrequency (%)
4120 1
< 0.1%
3923 1
< 0.1%
3522 1
< 0.1%
3093 1
< 0.1%
3030 1
< 0.1%
2829 1
< 0.1%
2656 1
< 0.1%
2477 1
< 0.1%
2396 1
< 0.1%
2317 1
< 0.1%

oucollaborate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2662568
Minimum0
Maximum107
Zeros3754
Zeros (%)69.9%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:31.836710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile13.7
Maximum107
Range107
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.7059883
Coefficient of variation (CV)2.9590594
Kurtosis48.866167
Mean2.2662568
Median Absolute Deviation (MAD)0
Skewness5.7489408
Sum12163
Variance44.970279
MonotonicityNot monotonic
2023-04-04T17:07:31.994182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3754
69.9%
1 437
 
8.1%
2 262
 
4.9%
3 129
 
2.4%
4 101
 
1.9%
5 72
 
1.3%
6 62
 
1.2%
8 47
 
0.9%
9 44
 
0.8%
7 42
 
0.8%
Other values (53) 417
 
7.8%
ValueCountFrequency (%)
0 3754
69.9%
1 437
 
8.1%
2 262
 
4.9%
3 129
 
2.4%
4 101
 
1.9%
5 72
 
1.3%
6 62
 
1.2%
7 42
 
0.8%
8 47
 
0.9%
9 44
 
0.8%
ValueCountFrequency (%)
107 1
< 0.1%
95 1
< 0.1%
94 1
< 0.1%
85 1
< 0.1%
78 1
< 0.1%
64 1
< 0.1%
62 1
< 0.1%
60 1
< 0.1%
57 1
< 0.1%
55 1
< 0.1%

oucontent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct878
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.89119
Minimum0
Maximum3978
Zeros836
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:32.159617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median17
Q387
95-th percentile926.9
Maximum3978
Range3978
Interquartile range (IQR)86

Descriptive statistics

Standard deviation363.85016
Coefficient of variation (CV)2.2066077
Kurtosis17.082903
Mean164.89119
Median Absolute Deviation (MAD)17
Skewness3.543523
Sum884971
Variance132386.94
MonotonicityNot monotonic
2023-04-04T17:07:32.350342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 836
 
15.6%
1 556
 
10.4%
2 256
 
4.8%
3 105
 
2.0%
4 91
 
1.7%
10 83
 
1.5%
5 78
 
1.5%
7 73
 
1.4%
13 70
 
1.3%
12 69
 
1.3%
Other values (868) 3150
58.7%
ValueCountFrequency (%)
0 836
15.6%
1 556
10.4%
2 256
 
4.8%
3 105
 
2.0%
4 91
 
1.7%
5 78
 
1.5%
6 62
 
1.2%
7 73
 
1.4%
8 62
 
1.2%
9 53
 
1.0%
ValueCountFrequency (%)
3978 1
< 0.1%
3709 1
< 0.1%
3526 1
< 0.1%
3210 1
< 0.1%
3185 1
< 0.1%
3108 1
< 0.1%
3061 1
< 0.1%
2968 1
< 0.1%
2792 1
< 0.1%
2757 1
< 0.1%

ouelluminate
Real number (ℝ)

Distinct23
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24725172
Minimum0
Maximum47
Zeros4962
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:32.554523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum47
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5314383
Coefficient of variation (CV)6.1938427
Kurtosis298.59285
Mean0.24725172
Median Absolute Deviation (MAD)0
Skewness14.085105
Sum1327
Variance2.3453032
MonotonicityNot monotonic
2023-04-04T17:07:32.710365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4962
92.5%
1 184
 
3.4%
2 85
 
1.6%
3 41
 
0.8%
4 27
 
0.5%
9 13
 
0.2%
7 8
 
0.1%
6 8
 
0.1%
5 8
 
0.1%
10 7
 
0.1%
Other values (13) 24
 
0.4%
ValueCountFrequency (%)
0 4962
92.5%
1 184
 
3.4%
2 85
 
1.6%
3 41
 
0.8%
4 27
 
0.5%
5 8
 
0.1%
6 8
 
0.1%
7 8
 
0.1%
8 1
 
< 0.1%
9 13
 
0.2%
ValueCountFrequency (%)
47 1
 
< 0.1%
39 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
18 1
 
< 0.1%
17 3
0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 5
0.1%

questionnaire
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2483697
Minimum0
Maximum44
Zeros4660
Zeros (%)86.8%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:32.911791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.7
Maximum44
Range44
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6789213
Coefficient of variation (CV)2.9469807
Kurtosis12.18795
Mean1.2483697
Median Absolute Deviation (MAD)0
Skewness3.2875636
Sum6700
Variance13.534462
MonotonicityNot monotonic
2023-04-04T17:07:33.078409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 4660
86.8%
3 87
 
1.6%
13 64
 
1.2%
12 63
 
1.2%
9 54
 
1.0%
6 49
 
0.9%
10 46
 
0.9%
11 45
 
0.8%
14 44
 
0.8%
4 38
 
0.7%
Other values (17) 217
 
4.0%
ValueCountFrequency (%)
0 4660
86.8%
1 14
 
0.3%
2 14
 
0.3%
3 87
 
1.6%
4 38
 
0.7%
5 25
 
0.5%
6 49
 
0.9%
7 36
 
0.7%
8 30
 
0.6%
9 54
 
1.0%
ValueCountFrequency (%)
44 1
 
< 0.1%
32 1
 
< 0.1%
27 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
21 2
 
< 0.1%
20 5
 
0.1%
19 6
 
0.1%
18 10
0.2%
17 16
0.3%

quiz
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct336
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.432644
Minimum0
Maximum950
Zeros534
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:33.200145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145
median75
Q3104
95-th percentile193
Maximum950
Range950
Interquartile range (IQR)59

Descriptive statistics

Standard deviation68.825723
Coefficient of variation (CV)0.83493286
Kurtosis19.14395
Mean82.432644
Median Absolute Deviation (MAD)29
Skewness2.8385145
Sum442416
Variance4736.9801
MonotonicityNot monotonic
2023-04-04T17:07:33.328421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 534
 
9.9%
1 101
 
1.9%
74 98
 
1.8%
78 88
 
1.6%
73 85
 
1.6%
75 80
 
1.5%
79 77
 
1.4%
70 76
 
1.4%
76 74
 
1.4%
67 74
 
1.4%
Other values (326) 4080
76.0%
ValueCountFrequency (%)
0 534
9.9%
1 101
 
1.9%
2 33
 
0.6%
3 28
 
0.5%
4 19
 
0.4%
5 8
 
0.1%
6 13
 
0.2%
7 9
 
0.2%
8 6
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
950 1
< 0.1%
859 1
< 0.1%
774 1
< 0.1%
705 1
< 0.1%
665 1
< 0.1%
662 1
< 0.1%
656 1
< 0.1%
646 1
< 0.1%
633 1
< 0.1%
593 1
< 0.1%

resource
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct241
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.050494
Minimum0
Maximum2808
Zeros171
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:33.484962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median24
Q346
95-th percentile116
Maximum2808
Range2808
Interquartile range (IQR)36

Descriptive statistics

Standard deviation58.324259
Coefficient of variation (CV)1.574183
Kurtosis956.38447
Mean37.050494
Median Absolute Deviation (MAD)16
Skewness21.61053
Sum198850
Variance3401.7192
MonotonicityNot monotonic
2023-04-04T17:07:33.633727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 171
 
3.2%
1 138
 
2.6%
14 134
 
2.5%
4 128
 
2.4%
5 125
 
2.3%
3 124
 
2.3%
2 119
 
2.2%
6 116
 
2.2%
15 113
 
2.1%
9 111
 
2.1%
Other values (231) 4088
76.2%
ValueCountFrequency (%)
0 171
3.2%
1 138
2.6%
2 119
2.2%
3 124
2.3%
4 128
2.4%
5 125
2.3%
6 116
2.2%
7 109
2.0%
8 105
2.0%
9 111
2.1%
ValueCountFrequency (%)
2808 1
< 0.1%
617 1
< 0.1%
567 1
< 0.1%
515 1
< 0.1%
415 1
< 0.1%
412 1
< 0.1%
398 2
< 0.1%
384 1
< 0.1%
378 1
< 0.1%
358 1
< 0.1%

sharedsubpage
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.1 KiB
0
5254 
1
 
90
2
 
16
3
 
5
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5367
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5254
97.9%
1 90
 
1.7%
2 16
 
0.3%
3 5
 
0.1%
4 2
 
< 0.1%

Length

2023-04-04T17:07:33.790041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-04T17:07:33.908663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5254
97.9%
1 90
 
1.7%
2 16
 
0.3%
3 5
 
0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5254
97.9%
1 90
 
1.7%
2 16
 
0.3%
3 5
 
0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5367
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5254
97.9%
1 90
 
1.7%
2 16
 
0.3%
3 5
 
0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5367
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5254
97.9%
1 90
 
1.7%
2 16
 
0.3%
3 5
 
0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5254
97.9%
1 90
 
1.7%
2 16
 
0.3%
3 5
 
0.1%
4 2
 
< 0.1%

subpage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct276
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.668716
Minimum0
Maximum822
Zeros101
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:34.069361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median32
Q359
95-th percentile142.4
Maximum822
Range822
Interquartile range (IQR)44

Descriptive statistics

Standard deviation55.911795
Coefficient of variation (CV)1.1729243
Kurtosis28.295658
Mean47.668716
Median Absolute Deviation (MAD)20
Skewness3.9943387
Sum255838
Variance3126.1288
MonotonicityNot monotonic
2023-04-04T17:07:34.226375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 101
 
1.9%
12 97
 
1.8%
14 97
 
1.8%
18 94
 
1.8%
17 94
 
1.8%
15 92
 
1.7%
19 90
 
1.7%
16 90
 
1.7%
13 90
 
1.7%
29 87
 
1.6%
Other values (266) 4435
82.6%
ValueCountFrequency (%)
0 101
1.9%
1 75
1.4%
2 75
1.4%
3 80
1.5%
4 80
1.5%
5 77
1.4%
6 80
1.5%
7 79
1.5%
8 85
1.6%
9 78
1.5%
ValueCountFrequency (%)
822 1
< 0.1%
728 1
< 0.1%
714 1
< 0.1%
557 1
< 0.1%
548 1
< 0.1%
539 1
< 0.1%
534 1
< 0.1%
492 1
< 0.1%
491 1
< 0.1%
476 1
< 0.1%

url
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct139
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.857462
Minimum0
Maximum297
Zeros626
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size42.1 KiB
2023-04-04T17:07:34.362681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q318
95-th percentile58.7
Maximum297
Range297
Interquartile range (IQR)16

Descriptive statistics

Standard deviation22.281565
Coefficient of variation (CV)1.4996885
Kurtosis16.53292
Mean14.857462
Median Absolute Deviation (MAD)6
Skewness3.2809108
Sum79740
Variance496.46812
MonotonicityNot monotonic
2023-04-04T17:07:34.494088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 626
 
11.7%
1 545
 
10.2%
2 434
 
8.1%
3 357
 
6.7%
4 280
 
5.2%
5 225
 
4.2%
6 199
 
3.7%
7 170
 
3.2%
8 162
 
3.0%
10 148
 
2.8%
Other values (129) 2221
41.4%
ValueCountFrequency (%)
0 626
11.7%
1 545
10.2%
2 434
8.1%
3 357
6.7%
4 280
5.2%
5 225
 
4.2%
6 199
 
3.7%
7 170
 
3.2%
8 162
 
3.0%
9 142
 
2.6%
ValueCountFrequency (%)
297 1
< 0.1%
209 1
< 0.1%
197 1
< 0.1%
185 1
< 0.1%
183 1
< 0.1%
181 1
< 0.1%
179 1
< 0.1%
175 2
< 0.1%
171 1
< 0.1%
165 1
< 0.1%

Interactions

2023-04-04T17:07:28.144659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:11.578655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:13.888373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:15.581578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:17.389139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:18.962562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:20.540788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:22.104843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:23.783082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:25.207967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:26.664990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:28.292439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:12.484812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:14.068223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:15.735378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:17.532322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:19.099972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:20.686966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:22.243267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:23.928852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:25.340880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:26.815875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:28.461124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:12.614840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:14.195164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:15.915135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:17.696168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:19.237707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:20.825430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:22.355552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:24.117601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:25.448309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:26.971797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:28.590073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:12.719464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:14.309777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:16.017831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:17.817998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:19.392524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:20.964275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:22.498227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:24.222714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:25.584635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:27.064494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:28.757827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:12.839116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:14.469451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:16.133490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:17.959818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:19.558226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:21.088761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:22.626626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:24.368306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:25.717739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:27.211053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:28.893003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:13.013418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:14.628249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:16.287930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:18.149085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:19.712376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:21.220646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:22.772727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:24.485954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:25.893002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:27.358695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:29.036822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:13.148025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:14.781179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:16.447254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:18.265224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:19.863962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:21.366770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:22.908016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:24.613605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:26.020556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:27.468831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:29.158597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:13.270362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:14.933400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:16.641369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:18.397282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:19.995679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:21.523888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:23.228016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:24.750752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:26.137737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:27.594517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:29.293700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:13.440251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:15.088812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:16.775833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:18.553441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:20.131826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:21.642504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:23.354009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:24.857745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:26.238452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:27.729721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:29.423395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:13.603085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:15.237816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:16.930937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:18.689575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:20.270514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:21.795624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:23.502030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:24.974569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:26.372578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:27.883136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:29.554960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:13.757857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:15.392932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:17.051894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:18.827678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:20.394653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:21.955140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:23.625417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:25.069885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:26.517279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-04T17:07:27.995027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-04T17:07:34.648500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
forumngglossaryhomepageoucollaborateoucontentouelluminatequestionnairequizresourcesubpageurlfinal_resultsharedsubpage
forumng1.0000.3500.7560.094-0.0200.271-0.0460.4390.4040.5820.5980.1090.108
glossary0.3501.0000.4560.3100.3060.1350.2890.3170.3970.3970.1630.0500.042
homepage0.7560.4561.0000.3000.3610.1970.2710.6530.7000.7560.5210.1620.113
oucollaborate0.0940.3100.3001.0000.591-0.1750.5040.2920.3220.188-0.1790.1310.000
oucontent-0.0200.3060.3610.5911.000-0.2880.5750.3880.4220.247-0.1680.2060.025
ouelluminate0.2710.1350.197-0.175-0.2881.000-0.1090.0710.1490.2300.2210.0160.063
questionnaire-0.0460.2890.2710.5040.575-0.1091.0000.3760.2880.129-0.3080.1830.000
quiz0.4390.3170.6530.2920.3880.0710.3761.0000.5080.5220.2740.2560.039
resource0.4040.3970.7000.3220.4220.1490.2880.5081.0000.8240.3720.0200.081
subpage0.5820.3970.7560.1880.2470.2300.1290.5220.8241.0000.5910.1350.123
url0.5980.1630.521-0.179-0.1680.221-0.3080.2740.3720.5911.0000.1460.086
final_result0.1090.0500.1620.1310.2060.0160.1830.2560.0200.1350.1461.0000.015
sharedsubpage0.1080.0420.1130.0000.0250.0630.0000.0390.0810.1230.0860.0151.000

Missing values

2023-04-04T17:07:29.746355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-04T17:07:30.295946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

final_resultforumngglossaryhomepageoucollaborateoucontentouelluminatequestionnairequizresourcesharedsubpagesubpageurl
0Fail1306406000812053
1Pass258090835404163100264
2Fail22080100322042
3Pass35501500000589087
4Pass863341201700785405438
5Fail1001301001290153
6Fail550120012008780169
7Pass470732110000300361
8Fail20903820280017799013228
9Pass2049196601501602703614
final_resultforumngglossaryhomepageoucollaborateoucontentouelluminatequestionnairequizresourcesharedsubpagesubpageurl
5357Fail10303201200050113
5358Pass003701003880176
5359Fail3310116010042110177
5360Fail272013801600751203527
5361Pass100220000702030
5362Pass13602180220085480415
5363Fail27301900140955109630
5364Pass30610400311902013
5365Pass203490732051251140841
5366Pass9001906483014123220300

Duplicate rows

Most frequently occurring

final_resultforumngglossaryhomepageoucollaborateoucontentouelluminatequestionnairequizresourcesharedsubpagesubpageurl# duplicates
0Fail00100000000011
1Fail0020000000004
3Fail0020000010203
2Fail0020000000012
4Fail0030000000002
5Fail1030000000002